Political Science and Data Science: What we can learn from each other (IS Colloquium)

Political science is integrating computational methods like machine learning into its own toolbox. At the same time the awareness rises that the utilization of machine learning algorithms in our daily life is a highly political issue. These two trends - the integration of computational methods into political science and the political analysis of the digital revolution - form the ground for a new transdisciplinary approach: political data science. Interestingly, there is a rich tradition of crossing the borders of the disciplines, as can be seen in the works of Paul Werbos and Herbert Simon (both political scientists).
Building on this tradition and integrating ideas from deep learning and Hegel's philosophy of logic a new perspective on causality might arise.

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Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems